Lately, the term “Generative AI” has become a trending topic around the world thanks to the release of the publicly available AI models, like ChatGPT, Gemini, Claude, etc. As we all know, their capabilities were initially limited to understanding and generating texts, but soon after, they got their ability to perform the same thing on images as well. Talking more specifically about generative models for image data, there are actually plenty number of model variations we can use, in which every single of those has their own purpose. So far, I already got some of my articles about generative AI for image data published in Medium, such as Autoencoder and Variational Autoencoder (VAE). In today’s article, I am going to talk about another fascinating generative algorithm: The Neural Style Transfer.
NST was first introduced in a paper titled “A Neural Algorithm of Artistic Style” written by Gatys et al. back in 2015 [1]. It is explained in the paper that their main objective is to transfer the artistic style of an image (typically a painting) onto a different image, hence the name “Style Transfer.” Look at some examples in Figure 1 below, where the…